MODELING TRIAL AND INTERPRETATION OF RESULTS FOR MACHINE LEARNING APPLICATIONS IN INFRASTRUCTURE MAINTENANCE AND MANAGEMENT

Abstract

In this paper, we discuss the possibility of using Artificial Intelligence (AI) in infrastructure management, focusing on the analytical performance and interpretability of models. In particular, the paper outlines the mathematical background of ensemble learning methods, such as XGBoost, LightGBM, CatBoost, RandomForest, and decision tree analysis, which have recently achieved good results in machine learning applications. We report on the results of trial estimations of bridge deterioration determined using these methods. In addition, this paper discusses the analysis results from the viewpoint of AI application in infrastructure management, considering the characteristics of each method.